




'''simple docstring'''







from __future__ import annotations

class 	_snake_case:






 def __init__(self       :  Optional[Any]    ,		a       :  Union[str, Any]    )       ->   None:






    """simple docstring"""


    A__       =     order

    # a_{0} ... a_{k}
    A__       =     [1.0] + [0.0] * order
    # b_{0} ... b_{k}
    A__       =     [1.0] + [0.0] * order

    # x[n-1] ... x[n-k]
    A__       =     [0.0] * self.order
    # y[n-1] ... y[n-k]
    A__       =     [0.0] * self.order






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  Optional[Any]    ,		a       :  Tuple    )       ->   None:






    """simple docstring"""


    if len(a    ) < self.order:
       A__       =     [1.0, *a_coeffs]

    if len(a    ) != self.order + 1:
       A__       =     (
           f"""Expected a_coeffs to have {self.order + 1} elements """
           f"""for {self.order}-order filter, got {len(a    )}"""
       )
       raise ValueError(a    )

    if len(a    ) != self.order + 1:
       A__       =     (
           f"""Expected b_coeffs to have {self.order + 1} elements """
           f"""for {self.order}-order filter, got {len(a    )}"""
       )
       raise ValueError(a    )

    A__       =     a_coeffs
    A__       =     b_coeffs






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  Dict    )       ->   float:






    """simple docstring"""


    A__       =     0.0

    # Start at index 1 and do index 0 at the end.
    for i in range(1    ,		self.order + 1    ):
       result += (
           self.b_coeffs[i] * self.input_history[i - 1]
           - self.a_coeffs[i] * self.output_history[i - 1]
       )

    A__       =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

    A__       =     self.input_history[:-1]
    A__       =     self.output_history[:-1]

    A__       =     sample
    A__       =     result

    return result









"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor

@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            lowerCAmelCase    		=			AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            model.to(lowercase					)

            from datasets import load_dataset

            lowerCAmelCase    		=			load_dataset("""nielsr/rvlcdip-demo"""					)

            lowerCAmelCase    		=			dataset["""train"""][0]["""image"""].convert("""RGB"""					)

            lowerCAmelCase    		=			image_processor(lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
                  lowerCAmelCase    		=			outputs.logits

            lowerCAmelCase    		=			torch.Size((1, 16)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [-0.4_158, -0.4_092, -0.4_347] ,		device=lowercase ,		dtype=torch.float ,		)
            self.assertTrue(torch.allclose(logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






